From 054e2b1954aafb15b0e983180dda309cfd5d831f Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 12 May 2016 20:36:11 +0000
Subject: [PATCH] not sure
---
src/parser.c | 36 +++++++++++++++++++++++++++++++++---
1 files changed, 33 insertions(+), 3 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index 6c88fd5..5a9d0a3 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -432,6 +432,7 @@
learning_rate_policy get_policy(char *s)
{
+ if (strcmp(s, "random")==0) return RANDOM;
if (strcmp(s, "poly")==0) return POLY;
if (strcmp(s, "constant")==0) return CONSTANT;
if (strcmp(s, "step")==0) return STEP;
@@ -497,7 +498,7 @@
} else if (net->policy == SIG){
net->gamma = option_find_float(options, "gamma", 1);
net->step = option_find_int(options, "step", 1);
- } else if (net->policy == POLY){
+ } else if (net->policy == POLY || net->policy == RANDOM){
net->power = option_find_float(options, "power", 1);
}
net->max_batches = option_find_int(options, "max_batches", 0);
@@ -852,6 +853,18 @@
fwrite(l.filters, sizeof(float), num, fp);
}
+void save_batchnorm_weights(layer l, FILE *fp)
+{
+#ifdef GPU
+ if(gpu_index >= 0){
+ pull_batchnorm_layer(l);
+ }
+#endif
+ fwrite(l.scales, sizeof(float), l.c, fp);
+ fwrite(l.rolling_mean, sizeof(float), l.c, fp);
+ fwrite(l.rolling_variance, sizeof(float), l.c, fp);
+}
+
void save_connected_weights(layer l, FILE *fp)
{
#ifdef GPU
@@ -889,6 +902,8 @@
save_convolutional_weights(l, fp);
} if(l.type == CONNECTED){
save_connected_weights(l, fp);
+ } if(l.type == BATCHNORM){
+ save_batchnorm_weights(l, fp);
} if(l.type == RNN){
save_connected_weights(*(l.input_layer), fp);
save_connected_weights(*(l.self_layer), fp);
@@ -943,8 +958,8 @@
if(transpose){
transpose_matrix(l.weights, l.inputs, l.outputs);
}
- //printf("Biases: %f mean %f variance\n", mean_array(l.biases, l.outputs), variance_array(l.biases, l.outputs));
- //printf("Weights: %f mean %f variance\n", mean_array(l.weights, l.outputs*l.inputs), variance_array(l.weights, l.outputs*l.inputs));
+ //printf("Biases: %f mean %f variance\n", mean_array(l.biases, l.outputs), variance_array(l.biases, l.outputs));
+ //printf("Weights: %f mean %f variance\n", mean_array(l.weights, l.outputs*l.inputs), variance_array(l.weights, l.outputs*l.inputs));
if (l.batch_normalize && (!l.dontloadscales)){
fread(l.scales, sizeof(float), l.outputs, fp);
fread(l.rolling_mean, sizeof(float), l.outputs, fp);
@@ -960,6 +975,18 @@
#endif
}
+void load_batchnorm_weights(layer l, FILE *fp)
+{
+ fread(l.scales, sizeof(float), l.c, fp);
+ fread(l.rolling_mean, sizeof(float), l.c, fp);
+ fread(l.rolling_variance, sizeof(float), l.c, fp);
+#ifdef GPU
+ if(gpu_index >= 0){
+ push_batchnorm_layer(l);
+ }
+#endif
+}
+
void load_convolutional_weights_binary(layer l, FILE *fp)
{
fread(l.biases, sizeof(float), l.n, fp);
@@ -1053,6 +1080,9 @@
if(l.type == CONNECTED){
load_connected_weights(l, fp, transpose);
}
+ if(l.type == BATCHNORM){
+ load_batchnorm_weights(l, fp);
+ }
if(l.type == CRNN){
load_convolutional_weights(*(l.input_layer), fp);
load_convolutional_weights(*(l.self_layer), fp);
--
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